Head to head · Open-weight models you run yourself · October 2026 research run
DiffusionGemma 26B A4B IT vs MiniCPM-o 4.5
MiniCPM-o 4.5 scores 61 (C) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 2 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk and agent interface. Both do open-weight models you run yourself.
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DiffusionGemma 26B A4B ITGoogle DeepMind · Open-weight model
D50/100#94 of 100 open-weight models
MiniCPM-o 4.5OpenBMB · Open-weight model
C61/100#42 of 100 open-weight models
Which one, for what
Best for Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4
Ahead on
- Risk, 70 against 40
- Agent interface, 70 against 35
Watch for
No quantised build from Google, so the smallest official build needs the 64 GB band
Best for Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.
Ahead on
- Reach, 90 against 50
- Documentation and upkeep, 66 against 60
Watch for
Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni
Score by category
| Category | Weight this run | DiffusionGemma 26B A4B IT | MiniCPM-o 4.5 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 50 | 90 | MiniCPM-o 4.5 +40 |
| Risk | 20%20 | 70 | 40 | DiffusionGemma 26B A4B IT +30 |
| Agent interface | 10%10 | 70 | 35 | DiffusionGemma 26B A4B IT +35 |
| Documentation and upkeep | 10%10 | 60 | 66 | MiniCPM-o 4.5 +6 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 50/100 | C 61/100 |
Facts side by side
| Fact | DiffusionGemma 26B A4B IT | MiniCPM-o 4.5 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | OpenBMB |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-07-15 | 2026-10-16 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 2.5/5 (4) | 2.8/5 (4) |
Verdicts
DiffusionGemma 26B A4B IT
A fast local generation cousin for low-concurrency work on text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. It needs about 62 GB for Google's BF16 files and runs only in Transformers or SGLang. The main risk is quality, since Google's own figures put it below Gemma 4 26B A4B, and it's labelled experimental.
MiniCPM-o 4.5
A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.
Before you call either
DiffusionGemma 26B A4B IT
- Serve it with
sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-codeon SGLang v0.5.22 or later, then call /v1/chat/completions - In Transformers, install
transformers>=5.11.0and loadDiffusionGemmaForBlockDiffusionwithdtype="auto"; generation_config.json already holds the recommended sampler - Turn thinking on with
enable_thinking=Truein apply_chat_template or<|think|>at the start of the system prompt, read the reasoning between<|channel>thoughtand<channel|>, and drop it from history before the next turn - Don't rely on temperature, top_p, logit_bias or JSON Schema in SGLang, which ignores or rejects them for this model; validate JSON yourself
- Streaming arrives one 256-token canvas at a time, not token by token
- Put images before text in the prompt, and pick an image token budget of 70 to 1,120 by how much detail the task needs
MiniCPM-o 4.5
- Quickest start for text and images is
ollama run openbmb/minicpm-o4.5, about 6 GB - With vLLM 0.16.0 or later pass
--trust-remote-code, and sendstop_token_ids[151643, 151645] as OpenBMB's guide does - vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni
- In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with
--mmproj, and use--jinja --reasoning-budget 0to turn thinking off - Keep prompts within 40,960 tokens
- Don't rely on tool calls or JSON output, neither is documented; validate any you get
Questions
Which is better for AI agents, DiffusionGemma 26B A4B IT or MiniCPM-o 4.5?
MiniCPM-o 4.5 scores 61 (C) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 2 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk and agent interface.
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Machine-readable
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/compare/diffusiongemma-26b-a4b-it-vs-minicpm-o-4-5.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/diffusiongemma-26b-a4b-it.json·/api/v1/tools/minicpm-o-4-5.json - From a terminal
anchor compare diffusiongemma-26b-a4b-it minicpm-o-4-5(the CLI) - Over MCP
compare_tools {"a": "diffusiongemma-26b-a4b-it", "b": "minicpm-o-4-5"}at/mcp, no key